Introduction


What is Ethnic Density?

Ethnic density is defined as the composition of each ethnic group residing in a geographical area of a given size (usually a fairly large geographical area, known as Lower Super Output Area (LSOA) which consists of around 1500 residents).

Here’s an image of London showing the ethnic density or ethnic composition of the city:

Ethnic Density of London

Ethnic Density of London


In the image above, the White British ethnic density is very high (indicated by the dominant dark green colour) across the city but especially in the outskirts of central London. There are pockets of high Asian ethnic density (dark blue), in the East and West of London. Non-British White ethnic groups (yellow) and Black ethnic groups (pink) have a reasonably high ethnic density in and around central London.

The ethnic densities vary across London, and different ethnic groups are dominant across different parts of London.


Measuring ethnic density using the ethnic density score

Based on where one lives and their ethnicity, every individual can be assigned with an ethnic density score. This score is simply the own ethnic groups (or own-group) ethnic density in the area they live in.

Calculating ethnic density score:

  A residential area - "Area A" - has a total of 1500 residents.
  
  The ethnic composition in Area A is: 
      500 residents of Indian descent, 
      250 residents of British descent and 
      750 residents of African descent.
      
  The Indian Ethnic Density would be 500 divided by the total number of 
  residents 1500 (0.33). 
  That is, 33% of all individuals in this area are of Indian descent.
  
  The British Ethnic Density would be 250 / 1500 (0.166). 
  
  The African Ethnic Density would be 750 / 1500 (0.50). 
  
  For Someone of Chinese descent who moves in to Area A, their ethnic density 
  score would be 1 divided by 1500 = ~0.00. Which indicates they live in an 
  area of low own-group ethnic density. 
  
  Someone of African descent would have an ethnic density of 50%. The African 
  person is livinig in an area of __high ethnic density__ (because there are
  more of this ethnic group in Area A, relative to any other ethnic groups). 

The ethnic density of a person hence is indicative of the type of area they live in, in terms of their ethnic composition. So whether they live in high ethnic density area, which is where there are more individuals of their own ethnicity or low ethnic density area, which is more individuals of another ethnicity in their residential area.


Why is Ethnic Density important?


Some studies are reporting that, in a multicultural cities, ethnic minorities living in areas where there are higher proportions of ethnic minority ethnicity may be better off (but in some cases worse) in terms of their mental and physical health relative to ethnic minority groups living in areas with larger proportions of the host ethnicity. This, beneficial effect on health by virtue of the ethnic composition in their residential area, is known as the ethnic density effect.

The figure below is an example suggesting the reporting levels of psychotic symptoms on relevant measures decreases among individuals living in areas of higher own-group ethnic density.

The Ethnic Density effect and Levels of Reporting Psychotic symptoms among White British women, Du Preez et al, 2016

The Ethnic Density effect and Levels of Reporting Psychotic symptoms among White British women, Du Preez et al, 2016


Another example (Figure below), of the ethnic density effect in play in a majority of the ethnicities presented below. There seems to be a reverse effect in White British ethnic group.

Differing effects of Ethnic Density in Different Ethnicities, Das Munshi et al, 2012

Differing effects of Ethnic Density in Different Ethnicities, Das Munshi et al, 2012


The figure above shows that the ethnic density effect may not manifest consistently among all ethnic groups, but there is evidence of a protective effect against mental health outcomes in most ethnic groups.


Studies demonstrating the positive Ethnic Density Effect on Mental Health

This ethnic density “effect” was first reported in 1939 a study by Faris and Dunham. Their study based in Chicago showed that White people, living in areas where Black ethnic groups were predominant, had a higher rate of schizophrenia (137.4 cases per 100,000), compared to the Black residents (39.4 cases per 100,000), where the overall area prevalence rate for schizophrenia was 50.4 cases per 100,000. In another study, Halpern and Nazroo used a nationwide community survey in England and Wales to explore the association of ethnic density and reported on levels of psychiatric symptoms. They showed a negative correlation of own-group ethnic density with neurotic symptoms, such as fatigue, sleep, depression and anxiety, (r = -0.087). That is, with a increase in ethnic density, there is a decrease in the levels of neurotic symptoms. Similarly, they found a negative association of ethnic density with psychotic symptoms (r = -0.113).

Studies demonstrating the a complex mechanism of the effect

Varying degrees of the effects of ethnic density (own-group or combined ethnic minority) on physical and mental health demonstrated positive effects of ethnic density on health outcomes but also detrimental effects in some ethnicities and not others. For example, among Black groups this association is largely reversed with increased risk of premature and all-cause mortality among Black groups with increasing Black ethnic density. The mechanism of the ethnic density effect is complex and requires a deeper understanding of ethnic groups and cultures.

Ethnic Density Effect and Suicidality

There is some evidence of the ethnic density effect being protective, for ethnic minority groups in the community, against suicide-related behaviours. In 2012, a review was published summarising the effect of ethnic density on mental health outcomes, which included suicide-related behaviours (2 studies) [Shaw et al, 2012]. Both studies found reduced risk of self-harm behaviour and completed suicide among ethnic minority groups with increasing ethnic density.

In one study, the rates of A&E attendance for self-harm were compared among White, African-Caribbean and Asian groups. They found that, as the ethnic minority densities increased, the self-harm referral rates of ethnic minorities fell relative to White self-harm referral rate with a risk ratio (RR) of 1.24 (95% CI: 0.69 – 2.10) in lower ethnic minority density versus an RR of 0.61 (0.47 – 0.79) in higher ethnic minority density areas.

In the other study, Neeleman used coroner’s records for completed suicide data to determine subjects’ ethnicity background to generate White and non-White ethnic density for each subject. They found that, as ethnic minority density increased, suicide rates were higher among the White ethnic group with an RR of 1.18 (1.02 – 1.37) and lower among ethnic minority groups RR of 0.75 (0.59 – 0.96).


Aim


Individuals diagnosed with certain mental disorders seen in secondary mental health care have a particularly high risk of suicide mortality compared to the general population. Whether the ethnic density effect has any impact on this risk is not clear.

The aim is to determine if there is an association of ethnic density with completed suicide in this secondary mental health care setting. In other words, this project will aim to study whether living in an area of high or low ethnic density (i.e. surrounded more by people of the same ethnicity or not) has any effect on completed suicide, in mental illness.


The Data


Data source

The data is derived from a mental health clinical trust in South London and provides mental healthcare for an area with a population of around 1.4 million residents, to individuals, who are referred by GPs, privately referred, A&E and self-referrals, seeking treatment for mild to severe mental health problems.

The trust uses electronic system to record day-to-day patient interactions (medical, demographic, clinical intervention etc) in either structured notes or free-text fields.

In 2008, a research facility was founded which used this a pseudonymised version of this electronic system from the South London trust for research and clinical audit purposes. Currently there are ~270000 records in this research database. For this project, a subset of patients, and related variables, were extracted based on an inclusion criteria (see below) to create the dataset for this project.


The Cohort

The dataset consists of 47851 patients.

The patients in the dataset were included if they met the following inclusion criteria:

  1. They had an active referral (in the form of face to face contact) at any point between the observation window of 1st of January 2008 and 31st of December 2014.

  2. They had a clinical diagnosis of depression, schizophrenia, schizoaffective, bipolar disorder, manic disorder and alcohol abuse. For patients with multiple diagnoses, the date of diagnosis closest to the observation start date was selected.

  3. They had an area-level address (LSOA code) recorded (to merge with census data). For patients with multiple area-level addresses, the closest address to the date of diagnosis was selected.

  4. They had a known ethnicity recorded (each patients’ ethnicity and ethnic composition in their LSOA was used to assign an ethnic density score) .

Diagnoses

Each individual in the cohort is diagnosed with one or more of the disorders mentioned in the table below.

Diagnosis N Number of Suicides
Schizophrenia
No 38091 190
Yes 9438 72
Schizoaffective
No 46199 252
Yes 1330 10
Bipolar
No 42197 216
Yes 5332 46
Substance Abuse
No 30030 169
Yes 17499 93
Depressive
No 27023 152
Yes 20506 110
Manic Disorder
No 45487 251
Yes 2042 11

Data notes

Suicide is rare event and there are far more patients who do not die by suicide compared to those do in this mental health setting. This means analysing the data will require taking this unbalance into account in order to study the association of ethnic density with death by suicide with biases.

The next file is a run through of the data cleaning process. The resulting dataset is used for exploration and analysis.